Unsupervised Skin Feature Tracking with Deep Neural Networks

arXiv:2405.04943v1 Announce Type: new
Abstract: Facial feature tracking is essential in imaging ballistocardiography for accurate heart rate estimation and enables motor degradation quantification in Parkinson's disease through skin feature tracking. While deep convolutional neural networks have shown remarkable accuracy in tracking tasks, they typically require extensive labeled data for supervised training. Our proposed pipeline employs a convolutional stacked autoencoder to match image crops with a reference crop containing the target feature, learning deep feature encodings specific to the object category …

abstract accuracy arxiv convolutional convolutional neural networks cs.cv data disease feature heart imaging networks neural networks parkinson parkinson's parkinson's disease quantification rate supervised training tasks through tracking training type unsupervised while